Micro-mechanical perspective on the role of particle shape in shearing of sands
Bibliographic record
Abstract
This study presents a micro-mechanical perspective on the role of realistic particle shapes in shearing of sands through calibration with physical experiments and parametric studies using discrete element method (DEM) to reveal the different mechanics between irregular particles and spherical particles incorporating rolling resistance (μr). To achieve this goal, particle shapes of Toyoura sand were captured by micro-computed tomography and reconstructed using a clump generation algorithm in DEM. The use of clumps in DEM led to closer match with experimental results of triaxial tests at various porosities and confining pressures, which could not be achieved by sphere models with μr. With realistic particle shapes, initial rotations of clumps were allowed until they became interlocked, while the load resistance built up gradually. High coordination numbers were observed, with force chains relatively evenly distributed. In contrast, the use of μr in sphere models promoted the formation of voids that were sustained during the loading process, because rotational motions of particles were hampered by μr and they did not easily collapse into the voids. This was accompanied by polarised strong force network around the voids, leading to more dilative and brittle macroscopic behaviour than observed in physical experiments. These findings illustrate that particle shapes cannot be sufficiently replaced by the use of sphere models and μr due to intrinsic differences in their micro-mechanics.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".